Results 231 to 240 of about 25,328 (252)

Non-Boltzmann dynamics in networks of spiking neurons

[Proceedings] 1991 IEEE International Joint Conference on Neural Networks, 1991
Networks of spiking neurons in which spikes are fired as a Poisson process are studied. The state of a cell is determined by the instantaneous firing rate, and in the limit of high firing rates the model reduces to that studied by Hopfield. The inclusion of spiking results in several features including a noise-induced asymmetry between on and off ...
Michael C. Crair, William Bialek
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Routing in a Delay Tolerant Network with Spiking Neurons

ICC 2019 - 2019 IEEE International Conference on Communications (ICC), 2019
We consider a dynamic reservoir of spiking neurons to generate routing decisions for bundle transmissions in a delay tolerant network (DTN). The reservoir transforms the context, as described by selected system metrics, and the temporal information that is contained in the network's contact list into a high-dimensional feature space, which allows ...
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A Cognitive Network Controller Based on Spiking Neurons

2018 IEEE International Conference on Communications (ICC), 2018
Cognitive networks plan, decide, and act at different layers of the protocol stack, based on perceived conditions of the network state and assigned rules. We introduce a Cognitive Network Controller (CNC) that relies on a spiking neural network to implement artificial cognition.
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Developing sensorimotor behavior with a network of spiking neurons

IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004, 2004
Artificial nervous systems have been realized with spiking neurons fabricated by VLSI. To explore ways of developing networks of such units through sensorimotor experience, a simulated vehicle, equipped with tactile and visual sensors was trained, using a biologically realistic Hebbian mechanism to navigate an obstacle course.
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Improved Izhikevich neurons for spiking neural networks

Soft Computing, 2011
Spiking neural networks constitute a modern neural network paradigm that overlaps machine learning and computational neurosciences. Spiking neural networks use neuron models that possess a great degree of biological realism. The most realistic model of the neuron is the one created by Alan Lloyd Hodgkin and Andrew Huxley.
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Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices

Applied Sciences (Switzerland), 2023
Bruno Golosio, JOSÉ Villamar, Gianmarco Tiddia   +2 more
exaly  

Reconfigurable Synaptic and Neuronal Functions in a V/VOx/HfWOx/Pt Memristor for Nonpolar Spiking Convolutional Neural Network

Advanced Functional Materials, 2022
Yaoyao Fu, , Fuwei Zhuge
exaly  

Photonic spiking neuron network

Réseau photonique de neurones impulsionnels Les réseaux neuromorphiques pour le traitement d'informations ont pris une placeimportante aujourd'hui notamment du fait de la montée en complexité des tâches à effectuer : reconnaissance vocale, corrélation d'images dynamiques, prise de décision rapide multidimensionnelle, fusion de données ...
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A New Fuzzy Spiking Neural Network Based on Neuronal Contribution Degree

IEEE Transactions on Fuzzy Systems, 2022
Jie Yang, Witold Pedrycz
exaly  

How Neuronal Noises Influence the Spiking Neural Networks’s Cognitive Learning Process: A Preliminary Study

Brain Sciences, 2021
Xu Yang, Miao Wang, Ziyi Huan
exaly